Multi-objective cooperative co-evolution of micro for RTS games

We investigate a multi-objective, cooperative, co-evolutionary algorithm to evolve control tactics for groups composed from multiple types of units in real-time strategy games. Such tactical control or micromanagement of units is called micro. Building on prior work, we cooperatively co-evolve micro for a ranged unit using a parameterized control algorithm along with micro for a melee unit using a pure potential fields approach and show that we can simultaneously co-evolve micro for melee and ranged units. These cooperatively co-evolved control algorithms for melee and ranged units evolve to work well together to defeat the default Starcraft II AI, even when outnumbered. We are also able to generate manually co-evolved AI that is significantly better than the default Starcraft II AI and defeat it to generate human competitive micro for controlling multiple types of units. Furthermore, using a multi-objective fitness function leads to a pareto front of near-optimal micro behaviors that range from fleeing while sustaining minimal damage to fighting and maximizing damage to opponents. Such a pareto front naturally provides a user or AI player a variety of micro behaviors suitable for the different types of situations encountered in real-time strategy games. We believe these results indicate the potential of our method for generating effective micro for multiple types of units in real-time strategy games with application in multi-agent control, robotics, and other heterogeneous system control problems.